The Absent Flesh of Law: Legal Bodies and Juridical Choreographies sutures dance studies—kinesthetic awareness, somatic memory, and performative potential—to the field of the legal humanities. This represents an epistemic shift that aims to resist the logocentric norms of knowing that reproduce colonial hierarchies. The dissertation simultaneously recuperates the agency of constituent power, especially in moments of public assembly. Specifically, it frames constitutional text as corporeal by exposing the body's aporetic disappearance: from French legal codes of the 17th century that structured race, to the contemporary elision of the freedom of assembly, to human rights discourse on bodily integrity. Through this disciplinary crossing, it presents a decolonial orientation to the legal subject, animated with all of its humanisms. Separate but entangled images of protesting ballerinas serve as the motivator for each chapter. Their internationally prolific circulation across publics enforces a cross-cultural method of comparison as the events of the image shuttle between the United States, Algeria, and France—nations linked by racial legacies and postcolonial histories. As such, this project draws upon Francophone and Arab material and furthers research in a comparative race studies. Attending to contemporary Black Lives Matter marches in the United States, demonstrations of the Hirak movement in Algeria, and pension protests in Paris—referenced in each photograph—the protesting ballerina grounds this framework to reveal themes of the disciplined body that is both compliant (to a technique) and resistant (to legal expectations). Each of these images begins well into the 21st-century yet points backward in time to more complex histories in which the body was obscured under law: 1) of confederate legacies constructed in the United States that propagate a racialized history, 2) of imperial rule in France during which ballet served state opulence and secured whiteness as property, and 3) of decolonial hope and postcolonial violence in Algeria. The final chapter opens toward international human rights discourse to call for renewed attention to bodily integrity, beyond a priori concepts of dignity that are circumscribed by a Western aesthetic tradition. Theoretically grounded in histories of the archive and the ephemerality of performance, this research draws upon interdisciplinary methods to communicate to scholars in both the legal humanities and in performance and dance studies. I supplement archival material—of police reports, juridical documents, and constitutional revision—with embodied perspectives learned from more than twenty years as a disciplined body in a dance studio. The tension of this pairing allows me to articulate what is lost when the law presumes linguistic form entirely, while the project's geopolitical triangulation reflects a commitment to postcolonial theory, francophone culture, and comparative race studies. As such, it dares to bring together otherwise disparate interlocuters, forcing a reconsideration of entrenched socio-political hierarchies of discipline.

Serverless, also known as function-as-a-service (FaaS), is an increasingly important paradigm in cloud computing. Developers register functions to a managed FaaS platform to serve user requests without the need to maintain their own servers. FaaS abstracts away the complexity of managing infrastructure, offers high availability, and automatically scales. However, today's FaaS platforms are often inefficient and unreliable, leaving developers with several complex application management challenges. Specifically, there are three key challenges: (1) minimizing cost while maintaining performance under varying load, (2) providing strong fault-tolerance guarantees in the presence of failures, and (3) improving debuggability and observability for distributed ephemeral functions. In this dissertation, we describe three new abstractions and build three systems to enhance the cost-efficiency, reliability, and debuggability of FaaS applications. We focus on two important categories of FaaS applications: compute-intensive, such as image recognition services, and data-centric, such as e-commerce web services. First, we address the challenge of cost efficiency for ML inference serving, a growing category of compute-intensive tasks. In particular, we tackle the key question of how to automatically configure and manage resources and models to minimize cost while maintaining high performance under unpredictable loads. Existing platforms usually require developers to manually search through thousands of model-variants, incurring significant costs. Therefore, we propose INFaaS, an automated model-less system where developers can easily specify performance and accuracy requirements without the need to specify a specific model-variant for each query. INFaaS generates model-variants from already trained models and efficiently navigates the large trade-off space of model-variants on behalf of developers to achieve application-specific objectives. By leveraging heterogeneous compute resources and efficient resource sharing, INFaaS guarantees application requirements while minimizing costs. Second, we address the challenge of providing fault tolerance while achieving high performance for data-centric applications. Existing FaaS platforms support these applications poorly because they physically and logically separate application logic, executed in cloud functions, from data management, done in interactive transactions accessing remote databases. Physical separation harms performance, and logical separation complicates efficiently providing fault tolerance. To solve this issue, we propose Apiary, a high-performance database-integrated FaaS platform for deploying and composing fault-tolerant transactional functions. Apiary wraps a distributed database engine and uses it as a unified runtime for function execution, data management, and operational logging. By physically co-locating and logically integrating function execution and data management, Apiary delivers similar or stronger transactional guarantees as comparable systems while significantly improving performance, cost, and observability. Finally, we delve into the challenge of debugging distributed data-centric applications. These applications are hard to debug because they share data across many concurrent requests. Currently, developers need to unravel the complex interactions of thousands of concurrent events to reproduce and fix bugs. To make debugging easier, we extend the tight integration between compute and data in Apiary and explore the synergy between the way people develop and debug their database-backed applications. We propose R^3, a "time travel" tool for data-centric FaaS applications that access shared data through transactions. R^3 allows for faithful replay of past executions in a controlled environment and retroactively execution of modified code on past events, making applications easier to maintain and debug. By recording concurrency information at transaction-level granularity, R^3 enables practical time travel with minimal overhead and supports most production DBMSs. We demonstrate how R^3 simplifies debugging for real, hard-to-reproduce concurrency bugs from popular open-source web applications

As the scale of both computing and data grows, developers are increasingly building distributed stateful systems in the cloud. However, these systems are challenging to build at scale because they must provide fault tolerance and consistency for stateful computations while managing both compute and data resources. Thus, we need new high-level abstractions that hide the complexity of distributed state management from developers. This dissertation proposes three such abstractions at multiple levels of the stack of a stateful cloud application. The first part of this dissertation targets cloud application developers, proposing Apiary, a database-oriented transactional function-as-a-service (FaaS) platform for stateful cloud applications. FaaS is an increasingly popular programming model because it abstracts away resource management concerns and reduces the complexity of cloud deployment, but existing FaaS platforms struggle to efficiently or reliably serve stateful applications. Apiary solves this problem by tightly integrating function execution with data management, improving FaaS performance on stateful applications by 2-68x while providing fault tolerance and strong transactional guarantees. The second part of this dissertation targets developers of the data management systems on which stateful cloud apps depend, proposing data-parallel actors (DPA), a framework for scaling data management systems. DPA targets an increasingly important class of data management systems called query serving systems, which are characterized by data-parallel, low-latency computations and frequent bulk data updates. DPA allows developers to construct query serving systems from purely single-node components while automatically providing critical properties such as data replication, fault tolerance, and update consistency. We use DPA to build a new query serving system, a simplified data warehouse based on MonetDB, and port existing ones, such as Druid, Solr, and MongoDB, enhancing them with new features such as a novel parallelism-optimizing data placement policy that improves query tail latency by 7-64%. The third part of this dissertation targets application developers utilizing multiple data management systems, proposing Epoxy, a protocol for providing ACID transactions across diverse data stores. Such applications are increasingly common because developers often use multiple data stores to manage heterogeneous data, for example doing transaction processing in Postgres and text search in Elasticsearch while storing image data in a cloud object store like AWS S3. To provide transactional guarantees for these applications, Epoxy adapts multi-version concurrency control to a cross-data store setting. We implement Epoxy for five data stores: Postgres, Elasticsearch, MongoDB, Google Cloud Storage, and MySQL, finding it outperforms existing distributed transaction protocols like XA while providing stronger guarantees and supporting more systems

The era of huge data necessitates highly efficient machine learning algorithms. Many common machine learning algorithms, however, rely on computationally intensive subroutines that are prohibitively expensive on large datasets. Oftentimes, existing techniques subsample the data or use other methods to improve computational efficiency, at the expense of incurring some approximation error. This thesis demonstrates that it is often sufficient, instead, to substitute computationally intensive subroutines with a special kind of randomized counterparts that results in almost no degradation in quality. The results in this thesis are based on techniques from the adaptive sampling literature. Chapter 1 begins with an introduction to a specific adaptive sampling problem: that of best-arm identification in multi-armed bandits. We first provide a formal description of the setting and the best-arm identification problem. We then present a general algorithm, called successive elimination, for solving the best-arm identification problem. The techniques developed in Chapter 1 will be applied to different problems in 2, 3, and 4. In Chapter 2, we discuss an how the k-medoids clustering problem can be reduced to a sequence of best-arm identification problems. We use this observation to present a new algorithm, based on successive elimination, that matches the prior state-of-the-art in clustering quality but reaches the same solutions much faster. Our algorithm achieves an n/logn reduction in sample complexity over prior state-of-the-art, where n is the size of the dataset, under general assumptions over the data generating distribution. In Chapter 3, we analyze the problem of training tree-based models. The majority of the training time for such models is in splitting each node of the tree, i.e., determining the feature and corresponding threshold at which to split each node. We show that the node-splitting subroutine can be reduced to a best-arm identification problem and present a state-of-the-art algorithm for training trees. Our algorithm depends only on the relative quality of each possible split, rather than explicitly depending on the size of the training dataset, and reduces the explicit dependence on dataset size n from O(n), for the most commonly-used prior algorithm, to O(1). Our algorithm applies generally to many tree-based models, such as Random Forests and XGBoost. In Chapter 4, we study the Maximum Inner Product Search problem. We observe that, as with the k-medoids and node-splitting problems, the Maximum Inner Product Search problem can be reduced to a best-arm identification problem. Armed with this observation, we present a novel algorithm for the Maximum Inner Product Search problem in high dimensions. Our algorithm reduces the explicit scaling with d, the dimensionality of the dataset, O(sqrt(d)) to O(1) under reasonable assumptions on the data. Our algorithm has several advantages: it requires no preprocessing of the data, naturally deals with the addition or removal of new datapoints, and includes a hyperparameter to trade off accuracy and efficiency. Chapter 5 concludes this thesis with a summary of its contributions and possible directions for future work

The objective of this research is to achieve accurate and scalable bridge health monitoring (BHM) by learning, integrating, and generalizing the monitoring models derived from drive-by vehicle vibrations. Early diagnosis of bridge damage through BHM is crucial for preventing more severe damage and collapses that could lead to significant economic and human losses. Conventional BHM approaches require installing sensors directly on bridges, which are expensive, inefficient, and difficult to scale up. To address these limitations, this research uses vehicle vibration data when the vehicle passes over the bridge to infer bridge conditions. This drive-by BHM approach builds on the intuition that the recorded vehicle vibrations carry information about the vehicle-bridge interaction (VBI) and thus can indirectly inform us of the dynamic characteristics of the bridge. Advantages of this approach include the ability for each vehicle to monitor multiple bridges economically and eliminating the need for on-site maintenance of sensors and equipment on bridges. Though the drive-by BHM approach has the above benefits, implementing it in practice presents challenges due to its indirect measurement nature. In particular, this research tackles three key challenges: 1) Complex vehicle-bridge interaction. The VBI system is a complex interaction system, making mathematical modeling difficult. The analysis of vehicle vibration data to extract the desired bridge information is challenging because the data have complex noise conditions as well as many uncertainties involved. 2) Limited temporal information. The drive-by vehicle vibration data contains limited temporal information at each coordinate on the bridge, which consequently restricts the drive-by BHM's capacity to deliver fine-grained spatiotemporal assessments of the bridge's condition. 3) Heterogeneous bridge properties. The damage diagnostic model learned from vehicle vibration data collected from one bridge is hard to generalize to other bridges because bridge properties are heterogeneous. Moreover, the multi-task nature of damage diagnosis, such as detection, localization, and quantification, exacerbates the system heterogeneity issue. To address the complex vehicle-bridge interaction challenge, this research learns the BHM model through non-linear dimensionality reduction based on the insights we gained by formulating the VBI system. Many existing physics-based formulations make assumptions (e.g., ignoring non-linear dynamic terms) to simplify the drive-by BHM problem, which is inaccurate for damage diagnosis in practice. Data-driven approaches are recently introduced, but they use black-box models, which lack physical interpretation and require lots of labeled data for model training. To this end, I first characterize the non-linear relationship between bridge damage and vehicle vibrations through a new VBI formulation. This new formulation provides us with key insights to model the vehicle vibration features in a non-linear way and consider the high-frequency interactions between the bridge and vehicle dynamics. Moreover, analyzing the high-dimensional vehicle vibration response is difficult and computationally expensive because of the curse of dimensionality. Hence, I develop an algorithm to learn the low-dimensional feature embedding, also referred to as manifold, of vehicle vibration data through a non-linear and non-convex dimensionality reduction technique called stacked autoencoders. This approach provides informative features for achieving damage estimation with limited labeled data. To address the limited temporal information challenge, this research integrates multiple sensing modalities to provide complementary information about bridge health. The approach utilizes vibrations collected from both drive-by vehicles and pre-existing telecommunication (telecom) fiber-optic cables running through the bridge. In particular, my approach uses telecom fiber-optic cables as distributed acoustic sensors to continuously collect bridge dynamic strain responses at fixed locations. In addition, drive-by vehicle vibrations capture the input loading information to the bridge with a high spatial resolution. Due to extensively installed telecom fiber cables on bridges, the telecom cable-based approach also does not require on-site sensor installation and maintenance. A physics-informed system identification method is developed to estimate the bridge's natural frequencies, strain and displacement mode shapes using telecom cable responses. This method models strain mode shapes based on parametric mode shape functions derived from theoretical bridge dynamics. Moreover, I am developing a sensor fusion approach that reconstructs the dynamic responses of the bridge by modeling the vehicle-bridge-fiber interaction system that considers the drive-by vehicle and telecommunication fiber vibrations as the system input and output, respectively. To address the heterogeneous bridge properties challenge, this research generalizes the monitoring model for one bridge to monitor other bridges through a hierarchical model transfer approach. This approach learns a new manifold (or feature space) of vehicle vibration data collected from multiple bridges so that the features transferred to such manifold are sensitive to damage and invariant across multiple bridges. Specifically, the feature is modeled through domain adversarial learning that simultaneously maximizes the damage diagnosis performance for the bridge with available labeled data while minimizing the performance of classifying which bridge (including those with and without labeled data) the data came from. Moreover, to learn multiple diagnostic tasks (including damage detection, localization, and quantification) that have distinct learning difficulties, the framework formulates a feature hierarchy that allocates more learning resources to learn tasks that are hard to learn, in order to improve learning performance with limited data. A new generalization risk bound is derived to provide the theoretical foundation and insights for developing the learning algorithm and efficient optimization strategy. This approach allows a multi-task damage diagnosis model developed using labeled data from one bridge to be used for other bridges without requiring training data labels from those bridges. Overall, this research offers a new approach that can achieve accurate and scalable BHM by learning, integrating, and generalizing monitoring models learned from drive-by vehicle vibrations. The approach enables low-cost and efficient diagnosis of bridge damage before it poses a threat to the public, which could avoid the enormous loss of human lives and property

Structural or spatial order at the nanometer/micron regime is an avenue to improve material properties. The field of photonics and metamaterials have shown that size-effects at these regimes, in combination with purposefully designed architected structures, can enhance mechanical and optical performance. A common approach to achieve these types of ordered structures is through colloidal self-assembly or direct laser writing of 3D structures. In this work, I propose using direct laser writing to fabricate colloidal particles and to fabricate complex 3D structures that have enhanced mechanical properties. In the first part of my work, I focus on colloidal self-assembly as a method to achieve order. Due to the limited chemistries and shapes of colloids available to self-assemble, a large majority of self-assembled structures remain elusive. I propose using two-photon lithography to fabricate micron-sized particles and experimentally study the effect of shape (both concave and convex) on the final self-assembled structure. This method allows for highly monodisperse fabrication of colloidal particles which can then be imaged using optical techniques due to their micron size. I fabricate colloidal conical shapes that self-assemble under entropic conditions (depletants) and tune the degree of assembly by changing the effective driving force through size. I then use a custom machine learning framework to identify these assembled structures (columnar grains) and recover self-assembly trends in which larger particles show a higher degree of self-assembly. Building upon this work, convex particles, specifically the Archimedean truncated tetrahedron, are also fabricated using the same framework and studied under another entropic condition (hard-particle interaction). These particles assemble in a six-fold symmetry upon interaction with an interface and transition to a three-fold symmetry upon application of a potential field. Analytical and computational results show that the six-fold symmetry state is a quasi-stable state and upon additional energy input, a transition occurs to achieve the lower energy state. In the second part of my work, I use two-photon lithography in conjunction with nanoclusters to enhance the direct laser writing process and improve the mechanical properties. I fabricate lattices with micron sized features and test them mechanically. The resulting nanocomposite lattices shows high stiffness and best-of-class energy absorbance by suppressing layer by layer collapse that is commonly seen with these types of structures